Method for reconstructing the optical properties of a medium using a combination of a plurality of mellin-laplace transforms of a magnitude comprising a time distribution of a received signal, and associated reconstruction system

ABSTRACT

A method reconstructing the optical properties of a medium using a reconstruction system has a radiation source lighting the medium and a detector receiving a signal transmitted by the medium. The steps include lighting the medium using a radiation source, receiving by the detector of a signal emitted by the medium, and processing, for a source-detector pair, of a first distribution of the signal received by the corresponding detector. Then computing the Mellin-Laplace transform, for a given order and a given variable, of a magnitude comprising the first distribution, the order being an integer, the variable being a real number, and reconstructing optical properties of the medium using the Mellin-Laplace transform of said magnitude. The computation step includes computing a plurality of Mellin-Laplace transforms of the magnitude for distinct values of the order, and the reconstruction step is carried out from a combination of the plurality of Mellin-Laplace transforms.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to French Patent Application No. 11 61832 filed Dec. 16, 2011. The entirety of the priority application is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to a method for reconstructing the optical properties of a medium using a reconstruction system having at least one radiation source capable of lighting a medium and at least one detector capable of receiving a signal transmitted by the medium.

BACKGROUND

The article “Optical tomography in medical imaging” by S. R. Arridge, published in the journal “Inverse Problems” in 1999, is known, citing the possible use of the Mellin-Laplace transform of order n for applications to optical tomography.

However, this article does not specifically outline how this transform can be used in the context of the reconstruction of the optical properties of a diffusing medium, with the advantages described below.

The aim of the invention is therefore to propose a method for reconstructing the optical properties of the medium as well as a reconstruction system advantageously using the Mellin-Laplace transform, in particular allowing access to optical diffusion properties that are usually difficult to establish, and for example the spatial distribution of the absorption or diffusion coefficients of a medium.

SUMMARY

To that end, the invention relates to a reconstruction method of the aforementioned type, wherein the computation step includes computing a plurality of Mellin-Laplace transforms of said magnitude for distinct values of the order, and in that the reconstruction step is carried out from a combination of the plurality of Mellin-Laplace transforms.

Reconstruction of the optical properties for example refers to:

-   -   the reconstruction of the absorption properties, the latter in         particular being characterized by the absorption coefficient,         denoted μ_(a),     -   the reconstruction of the diffusion properties, the latter in         particular being characterized by the reduced diffusion         coefficient μ′_(s) or the diffusion coefficient D, and     -   the reconstruction of the fluorescence properties, the latter in         particular being characterized by a response function (F) of a         fluorophore, or by a concentration c of a fluorophore, or by         another magnitude altogether expressing a quantity q of a         fluorophore, the latter for example being endogenous or         exogenous.

According to other advantageous aspects of the invention, the reconstruction method includes one or more of the following features, considered alone or according to all technically possible combinations:

-   -   the computation step comprises the computation of at least one         Mellin-Laplace transform of said magnitude for an order value         greater than or equal to 5, preferably greater than or equal to         8,     -   the computation step comprises the computation of n_(max)+1         Mellin-Laplace transforms, the order successively taking all         values comprised between 0 and n_(max), n_(max) being greater         than or equal to 5, preferably greater than or equal to 8,     -   the value of the variable is between 1 ns⁻¹ and20 ns⁻¹,     -   the method also includes:         -   the determination, for at least one source-detector pair, of             a first modeling function of a diffusion signal of the light             between the source and the detector in the medium,         -   the processing, for said at least one source-detector pair,             of a second distribution of the signal received by the             detector for a reference medium, the received signal being             transmitted by the reference medium following the lighting             of said medium by the source, and         -   the determination, for said at least one source-detector             pair, of a second modeling function of a diffusion signal of             the light between the source and the detector in the             reference medium,         -   and said magnitude depends on the first distribution, the             first modeling function, the second distribution, and the             second modeling function,     -   said magnitude is obtained by subtracting the product of the         second distribution and the first modeling function from the         product of the first distribution and the second modeling         function,     -   said magnitude is the ratio between the product of the first         distribution and the second modeling function and the product of         the second distribution and the first modeling function,     -   the or each radiation source comprises a pulsed light source,     -   the or each detector is a time-resolved detector, and     -   the optical properties include at least one element from amongst         the group of:         -   the light absorption properties, in particular characterized             by the absorption coefficient,         -   the diffusion properties, in particular characterized by the             reduced diffusion coefficient or the diffusion coefficient,             and         -   the fluorescence properties, in particular characterized by             a response function of a fluorophore, or by a concentration             of the fluorophore, or by a magnitude depending on the             quantity of the fluorophore.

The invention also relates to a reconstruction system including

-   -   at least one radiation source capable of lighting the medium,     -   at least one detector capable of receiving a signal transmitted         by the medium,     -   means for processing a distribution of the signal received by         the corresponding detector for at least one source-detector         pair,     -   means for computing the Mellin-Laplace transform, for a given         order and a given variable, of a magnitude having the         distribution, the order being an integer, the variable being a         real number, and     -   means for reconstructing the optical properties of the medium         using the Mellin-Laplace transform of said magnitude,

wherein the computation means are capable of computing the plurality of Mellin-Laplace transforms of said magnitude for distinct values of the order, and in that the reconstruction means are capable of reconstructing the optical properties of the medium from a combination of the plurality of Mellin-Laplace transforms.

According to another advantageous feature of the invention, the reconstruction system also includes computation means capable of computing at least one Mellin-Laplace transform of said magnitude for an order value greater than or equal to 5, preferably greater than or equal to 8.

BRIEF DESCRIPTION OF THE DRAWINGS

These features and advantages of the invention will appear upon reading the following description, provided solely as an example, and done in reference to the appended drawings, in which:

FIG. 1 is a diagrammatic illustration of a reconstruction system according to an example of the invention, capable of reconstructing an image of a medium,

FIG. 2 is a set of curves showing a time distribution of the signal received by the system of FIG. 1 and the Mellin-Laplace transforms of that time distribution computed for order values between 0 and 9,

FIG. 3 is a flowchart of a reconstruction method according to an example of the invention,

FIG. 4 is a set of images showing sensitivity maps for the computed Mellin-Laplace transforms, each image corresponding to a respective order value and comprised between 0 and 8,

FIG. 5 is a view of an image of the medium reconstructed with a reconstruction system and reconstruction method of the state of the art, and

FIG. 6 is a view similar to FIG. 5 with the reconstruction system and reconstruction method according to an example of the invention.

DETAILED DESCRIPTION

FIG. 1 shows a reconstruction system 10 designed to examine a medium 12 by acquiring an image of the medium 12, then processing that image.

The reconstruction system 10 includes a plurality of radiation sources 14 for lighting the medium 12 and a plurality of detectors 16 for receiving a signal transmitted by the medium 12. These radiation sources 14 and the detector 16 are for example arranged in a probe, not shown.

The system 10 includes an information processing unit 18 and a screen 20 for displaying an image of the medium.

In the described embodiment, the system 10 is a time system, and each radiation source 14 is a pulsed radiation source. Each detector 16 is a time-resolved detector, i.e. a detector making it possible to measure the time distribution of the arrival time of the photons, also called TCSPC (Time-Correlated Single Photon Counting).

According to the illustrated example, the medium 12 has an observation surface 22, in the form of a plane parallel to a longitudinal axis X and a transverse axis Y, and in contact with which the sources 14 and the detectors 16 can be applied. The medium 12 has an observation direction 24 extending along a vertical axis Z and substantially perpendicular to the observation surface 22.

In the example embodiment of FIG. 1, the medium 12 is a medium to be characterized, denoted MC and visible in FIG. 1. The medium to be characterized MC comprises animal or human biological tissue. The medium to be characterized MC is for example an area of an organ such as the brain, a breast, the prostate, the digestive tube, or other organs in which fluorophores can be injected. More generally, the medium to be characterized MC is a biological medium, for example an organ, for which one wishes to determine the optical diffusion properties, and in particular the spatial distribution of coefficients such as the absorption coefficient or the diffusion coefficient.

The medium to be characterized MC is a diffusing medium containing inclusions having different optical absorption or diffusion properties from those of the medium. A single inclusion 26 is shown in FIG. 1 for clarity of the drawing.

Each pulsed radiation source 14 comprises a pulsed light source 28 and an optical excitation fiber 30 connected to the pulsed source 28 for transmission of the light pulse to the medium 12. When such a fibrous source is used, the free end of the excitation fiber 30 is assimilated with the source s.

In one alternative not shown, each pulsed radiation source 14 comprises an optical excitation fiber 30 connected to a single pulsed light source shared by the plurality of radiation sources 14. According to this alternative, the system 10 also includes an optical switch or a multiplexer to select the excitation fiber 30 in which the light beam is sent.

Also alternatively, the set of pulsed radiation sources 14 is made up of a single pulsed light source and a mirror device of the MEMS (MicroElectroMechanical Systems) type, not shown, to scan the medium 12 with the light coming from the pulsed light source. Also alternatively, the source 14 is a multiplexed source, delivering light pulses with different wavelengths.

In the case of an examination of the surface 22 of the medium or a shallow examination of the medium 12, i.e. a depth of several millimeters to several centimeters, the wavelength of each pulsed radiation source 14 is preferably in the visible or near infrared region, i.e. between 500 nm and 1300 nm. The repetition rate is approximately 50 MHz, and in general between 10 MHz and 100 MHz.

The pulses transmitted by each pulsed radiation source 14 have a length between 10 picoseconds and 1 nanosecond, preferably between 10 picoseconds and 100 picoseconds, each pulsed light source 28 being able to generate a time width pulse lasting between 10 picoseconds and 1 nanosecond, preferably between 10 picoseconds and 100 picoseconds.

Each time-resolved detector 16, also denoted d, comprises an optical detection fiber 32 connected to a time-resolved detection module 34. When such a fibrous detector is used, the free end of the optical detection fiber 32 is assimilated with the detector d.

In the example embodiment of FIG. 1, the detection module 34 comprises a detection member 36 for each detector 16. In one alternative not shown, the detection module 34 comprises a detection member shared by several detectors 16, in particular a single detection member for all of the detectors 16.

The detection member 36 is for example a photomultiplier, an avalanche photodiode (APD), a single-photon avalanche diode (SPAD), or an image intensifier tube with one or more anodes (Multi-Channel Plate).

In this example, the probe is a compact probe for diagnosing certain cancers, such as a portable probe for diagnosing breast cancer, and endorectal probe for diagnosing prostate cancer, or a dermatological probe. Alternatively, the probe is an endoscopic probe such as a flexible probe for diagnosing digestive cancer. Alternatively, the probe is a device applicable to other organs.

The information processing unit 18 comprises a data processor 38 and a memory 40 associated with the processor 38.

The processing unit 18 comprises means 42 for processing, from at least one source 14-detector 16 pair, a first distribution B_(sd) of a signal received by the corresponding detector 16 for the medium to be characterized MC, the received signal being transmitted by the diffusing medium to be characterized MC following the lighting of said medium MC by the corresponding source 14. The corresponding source 14 and detector 16, respectively, are referenced by indices s and d, respectively.

In the described embodiment, the first distribution is a time distribution respectively denoted B_(sd)(t). The processing means 42 are preferably made in the form of one or more electronic boards connected to the detector 16 and making it possible to measure the time distribution of the arrival time of the photons.

Each pulsed light source 28 comprises a pulsed laser. Alternatively, each pulsed light source 28 comprises a laser diode. Also alternatively, each pulsed light source 28 comprises a constant light source whereof the light intensity is modulated into pulses of equivalent length by a rapid-closing device. In this way, the source can transmit a ray of light assuming the form of a time pulse.

According to this particular embodiment, the end of each excitation fiber 30 extends perpendicular to the observation surface 22, i.e. along the axis Z, or obliquely relative to the axis Z, so as to emit light that is slanted relative to the observation surface 22.

The memory 40 is capable of storing software 44 for computing a plurality of Mellin-Laplace transforms Y_(sd) ^((p,n)), for a variable p, also called width, and distinct given values of an order n, a magnitude Y_(sd)(t) comprising the first distribution B_(sd)(t), the order n being an integer and the variable p being a real number.

The Mellin-Laplace transform of order n and width p of a function f is denoted f^((p,n)), and verifies the following equation:

$\begin{matrix} {f^{({p,n})} = {\frac{1}{n!}{\int_{0}^{+ \infty}{\left( {p\; t} \right)^{n} \cdot {\exp \left( {{- p}\; t} \right)} \cdot {f(t)} \cdot {t}}}}} & (1) \end{matrix}$

where 1/n! represents a normalization term. The Mellin-Laplace transform f^((p,n)) of the function f is also written:

$\begin{matrix} {f^{({p,n})} - {\int_{- \infty}^{+ \infty}{W^{({p,n})} \cdot {f(t)} \cdot {t}}}} & (2) \end{matrix}$

where W^((p,n)) represents a time window defined by the following equation:

$\begin{matrix} {W^{({p,n})} = \frac{{H(t)} \cdot \left( {p\; t} \right)^{n} \cdot {\exp \left( {{- p}\; t} \right)}}{n!}} & (3) \end{matrix}$

with H(t) representing the known Heaviside function.

In FIG. 2, the function f is represented by the curve 70 in bold lines, and the time windows W^((p,n)) are represented by curves 71 to 80, with p equal to 2 ns⁻¹ and with the order n respectively taking the successive integer values between 0 and n_(max), with n_(max) equal to 9. The positions ((n+1)/p, pf^((p,n))) are referenced by points 81 to 90, for the same values of the width p and the order n. The area of the time windows W^((p,n)) is equal to 1/p, and the average position of said time windows W^((p,n)) is equal to (n+1)/p.

In general, the Mellin-Laplace transform f^((p,n)) of order n and width p of the function f can be considered like any function including the term

∫₀^(+∞)(p t)^(n) ⋅ exp (−p t) ⋅ f(t) ⋅ t

to within a multiplicative coefficient.

In the described embodiment, at least one Mellin-Laplace transform Y_(sd) ^((p,n)) is computed for a value of order n greater than or equal to 5, preferably greater than or equal to 8.

In the described embodiment, the order n assumes a value between 0 and a maximum value n_(max). The order n for example successively assumes all values between 0 and n_(max). In other words, n_(max)+1 Mellin-Laplace transforms Y_(sd) ^((p,n)) are then computed for the computation software 44. The maximum value n_(max) is greater than or equal to 5, preferably greater than or equal to 8.

The combination of Laplace transforms of different orders, and in particular small orders, i.e. n close to 0, with large orders, i.e. n≧5, makes it possible to account for the contribution to the detected signal of photons emitted at moments very shortly after the excitation pulse (small orders) as well as more distant moments (large orders), the latter being called delayed photons, as they are detected several nanoseconds after the excitation light pulse.

The described embodiment, the value of the width p is between 1 ns⁻¹ and 20 ns⁻¹, where ns is the abbreviation of nanosecond.

The memory 40 is also capable of storing software 46 for reconstructing optical properties of the medium 12 from a combination of the plurality of Mellin-Laplace transforms Y_(sd) ^((p,n)) of said magnitude Y_(sd) (t). The reconstruction software 46 is capable of reconstructing optical coefficients, such as the optical absorption coefficient μ_(a) or the diffusion coefficient D, from the spatial distribution. Alternatively, the reconstruction software 46 can reconstruct a fluorescence map of the medium 12.

In the described embodiment, the magnitude Y_(sd)(t) is a corrected signal making it possible to account for the instrument response, also called corrective distribution, and is described in more detail hereafter.

Alternatively, the magnitude on which the plurality of Mellin-Laplace transforms is computed is the first distribution B_(sd)(t).

Alternatively, the computation means 44 and the reconstruction means 46 are made in the form of programmable logic components or dedicated integrated circuits.

The operation of the imaging system 10 according to the invention is described hereafter using FIG. 3, which shows a flowchart of the image processing method according to the invention.

During step 100, the medium to be characterized MC is lit by the corresponding source(s) 14. In the event of a plurality of sources 14, the lighting of the medium 12 is done successively for each of the sources 14. When it is lit, the medium to be characterized MC transmits an optical diffusion signal in return, which may be detected by the or each detector 16, and the first distribution of the received signal B_(sd)(t) is processed, during step 110, for each source 14-detector 16 pair, also denoted (s, d), using the processing means 42.

The first time distribution B_(sd)(t) verifies the following equation:

B _(sd)(t)=IRF _(sd)(t)*G _(sd)(t)  (4)

where IRF_(sd)(t) represents the instrument response, i.e. the influence of the source s and the detector d on the first processed distribution, and G_(sd)(t) represents a first modeling function of a diffusion signal of a light between the source 14 and the detector 16 in the medium to be characterized MC.

S_(s)(t) denotes the time response of the source 14, also denoted s. In this example, the source 14 is a brief light pulse, often modeled by a periodic Dirac-type distribution. S_(s)(t) represents the time intensity of the emitted signal. When the source 14 is fibrous (light source coupled to an optical fiber), it is the end of the optical excitation fiber 30 that is then considered the source, as previously indicated.

D_(d)(t) denotes the time response of the detector 16, also denoted d. This response in particular indicates the amount of time between the arrival of a photon on the detector 16 and the detection of that photon. When the detector 16 is fibrous (detector coupled to an optical fiber), the end of the detection fiber 32 is considered the detector, as previously indicated.

The instrument response, denoted IRF_(sd)(t), is generally estimated, the index sd corresponding to an excitation source s and detector d pair. This instrument response is obtained by combining Ss(t) and Dd(t) as follows: IRF_(sd)(t)=S_(s)(t)*D_(d)(t), where * designates the convolution integral.

When one has several sources and several detectors, IRF_(sd)(t) is determined for each source-detector pair, as each source and each detector has its own response. In practice, this is done by placing a source (or an optical fiber end making up the source) across from the detector (or an optical fiber end making up the detector). This assumes careful alignment between the source and the detector, and takes time, in particular when the number of sources and detectors is increased. As an example, when there are 10 sources and 10 detectors, 100 instrument response functions IRF_(sd) must be determined.

During step 120, the instrument response IRF_(ad)(t) is taken into account using a second time distribution A_(sd)(t) and a second modeling function G_(sd) ⁰(t). The second time distribution A_(sd)(t) is processed for a reference medium MR and under the same experimental conditions as for the first time distribution B_(sd)(t).

The reference medium MR, also called ghost, is known in itself and is, for example, described in document FR 2 950 241 A1. The ghost MR has known optical characteristics. The ghost MR is, for example, a ghost having absorption μ_(a) ⁰ and diffusion D⁰ coefficients with median values for human tissues, for example equal to 0.2 cm⁻¹ and 10 cm⁻¹, respectively.

The processing of the second time distribution A_(sd)(t) is preferably done in a preliminary manner, before step 100, and the reference medium MR is then replaced by the medium to be characterized MC at the beginning of step 100.

Alternatively, the processing of the second time distribution A_(sd)(t) is done during step 120, which requires changing the medium 12, and replacing the medium to be characterized MC with the reference medium MR.

The second time distribution A_(sd)(t) then verifies the following equation:

A _(sd)(t)=IRF _(sd)(t)*G _(sd) ⁰(t)  (5)

The first and second modeling functions G_(sd)(t), G_(sd) ⁰(t) are for example Green functions, well known in the optical diffusion field. Each Green function G_(sd)(t), G_(sd) ⁰(t) represents the density of photons at the detector 16 when the medium 12, i.e. the reference medium MR for the first modeling function and the medium to be characterized MC for the second modeling function, respectively, is lit by the source 14, where s and d are the indices respectively designating the source 14 and the detector 16.

The corrected signal Y_(sd)(t) is then computed as a function of the first distribution B_(sd)(t), the first modeling function G_(sd)(t), the second distribution A_(sd)(t) and the second modeling function G_(sd) ⁰(t).

The corrected signal Y_(sd)(t) is the result of a comparison operation of the product of the second distribution A_(sd) and the first modeling function G_(sd) with the product of the first distribution B_(sd) and the second modeling function G_(sd) ⁰. The comparison operation comprises an arithmetic operation of the product of the second distribution A_(sd) and the first modeling function G_(sd) with the product of the first distribution B_(sd) and the second modeling function G_(sd) ⁰.

The arithmetic operation is, for example, a subtraction of the product of the second distribution A_(sd) and the first modeling function A_(sd) from the product of the first distribution B_(sd) and the second modeling function G_(sd) ⁰.

In the described embodiment, the product of the second distribution A_(sd)(t) and the first modeling function G_(sd)(t) and the product of the first distribution B_(sd)(t) and the second modeling function G_(sd) ⁰(t) are convolution integers, the first B_(sd)(t) and second A_(sd)(t) distributions being time distributions.

The corrected signal Y_(sd)(t) verifies the following equation:

Y _(sd)(t)=B _(sd)(t)*G _(sd) ⁰(t)−A _(sd)(t)*G _(sd)(t)  (6)

Alternatively, the arithmetic operation is the ratio between the product of the first time distribution B_(sd)(t) and the second modeling function G_(sd) ⁰(t) and the product of the second time distribution A_(sd)(t) and the first modeling function G_(sd)(t).

According to this alternative, the corrected signal Y_(sd)(t) then verifies the following equation:

$\begin{matrix} {{Y_{sd}(t)} = \frac{{B_{sd}(t)}*{G_{sd}^{0}(t)}}{{A_{sd}(t)}*{G_{sd}(t)}}} & (7) \end{matrix}$

Complementarily, the first modeling function G_(sd)(t) is approximated using the Green function G_(sd) ¹(t) for the medium to be characterized MC, and the following equations are then obtained:

$\begin{matrix} \begin{matrix} {{Y_{sd}(t)} = {{{B_{sd}(t)}*{G_{sd}^{0}(t)}} - {{A_{sd}(t)}*{G_{sd}^{1}(t)}}}} \\ {= {{{{IRF}_{sd}(t)}*{G_{sd}(t)}*{G_{sd}^{0}(t)}} - {{{IRF}_{sd}(t)}*{G_{sd}^{0}(t)}*{G_{sd}^{1}(t)}}}} \\ {= {{{IRF}_{sd}(t)}*{G_{sd}^{0}(t)}*\left( {{G_{sd}(t)} - {G_{sd}^{1}(t)}} \right)}} \\ {= {{A_{sd}(t)}*\left( {{G_{sd}(t)} - {G_{sd}^{1}(t)}} \right)}} \\ {= {{- {A_{sd}(t)}}*\left( {{\int{{G_{s}^{1}\left( {\overset{\rightarrow}{r},t} \right)}*{{\delta\mu}_{a}\left( {\overset{\rightarrow}{r},t} \right)}*{G_{d}^{1}\left( {\overset{\rightarrow}{r},t} \right)}{\overset{\rightarrow}{r}}}} +} \right.}} \\ \left. {\int{{\overset{\rightarrow}{\nabla}{G_{s}^{1}\left( {\overset{\rightarrow}{r},t} \right)}}*\delta \; {D\left( {\overset{\rightarrow}{r},t} \right)}*{\overset{\rightarrow}{\nabla}{G_{d}^{1}\left( {\overset{\rightarrow}{r},t} \right)}}{\overset{\rightarrow}{r}}}} \right) \end{matrix} & (8) \end{matrix}$

where μ_(a) and D respectively represent the absorption coefficient and the diffusion coefficient of the medium to be characterized MC,

IRF_(sd)(t) represents the instrument response,

G_(s)(r,t)=G(r_(s),r,t) is the Green function representing the density of photons at a location r of the medium MC when the medium MC is lit by the source s situated at r_(s),

G_(d)(r,t)=G(r_(d),r,t) is the Green function representing the density of photons at the location r of the medium MC when the medium to be characterized MC is lit by the source s situated at r_(d). This is also written G_(d)(r,t)=G(r,r_(d),t). Thus, this Green function also represents the density of photons at the detector (r_(d)) when the medium to be characterized MC is lit by a source situated at r.

In other words, G_(sd)(t) corresponds to the real values μ_(a)(r, t) and D(r, t), i.e. the desired values, and G_(sd) ¹(t) corresponds to approximate values μ¹ _(a)(r, t) and D¹(r, t), with μ_(a)(r, t)=μ¹ _(a)(r, t)+δμ_(a)(r, t) and D(r, t)=D¹(r, t)+δD(r, t). The process of reconstructing the optical properties is generally iterative, such that during each iteration, values μ¹ _(a)(r) and D¹(r) are obtained, to which the Green functions G_(sd) ¹(t) correspond for each source-detector pair sd. The aim of the reconstruction method is then to minimize δμ_(a)(r, t) and δD(r, t), which makes it possible to urge μ¹ _(a)(r, t) and D¹(r, t) toward μ_(a)(r, t) and D(r, t), respectively.

The coefficients μ_(a) and D are constant over time, and the values μ_(a)(r, t)=μ_(a)(r)∂(t) and D(r,t)=D(r)∂(t), ∂(t) represent a Dirac distribution at moment t.

The computation of the corrected signal Y_(sd)(t), also called corrected time distribution, therefore does away with precise knowledge of the instrument response IRF_(sd)(t) according to equations (6), (7) or (8). This makes it possible to avoid relatively lengthy and tedious experimental measurements in order to determine the instrument response of the imaging system 10.

Alternatively, the instrument response IRF_(sd)(t), representing the time distribution of the pulse generated by the radiation source and detected by the detector in the absence of a diffusive medium, is measured, for each source-detector pair (s, d), during a calibration operation during which the source s is placed across from the detector d in the absence of the medium 12.

During step 130, the Mellin-Laplace transforms Y_(sd) ^((p,n)) of the corrected distribution are then computed for different values of the order n between 0 and n_(max), preferably for all of the successive values between 0 and n_(max), according to the following equation:

$\begin{matrix} {Y_{sd}^{({p,n})} = {\sum\limits_{{i + j} = n}\; \left( {{B_{sd}^{({p,i})} \cdot G_{sd}^{0{({p,j})}}} - {A_{sd}^{({p,i})} \cdot G_{sd}^{({p,j})}}} \right)}} & (9) \end{matrix}$

However, the corrected distribution Y_(sd)(t) verifies the following equation:

Y _(sd)(t)=−A _(sd)(t)*(∫G _(s) ¹({right arrow over (r)},t)* δμ_(a)({right arrow over (r)},t)*G _(d) ¹({right arrow over (r)},t)d{right arrow over (r)}+∫{right arrow over (∇)}G _(s) ¹({right arrow over (r)},t)*δD({right arrow over (r)},t)*{right arrow over (∇)}G _(d) ¹({right arrow over (r)},t)d{right arrow over (r)})  (10)

Equations (9) and (10) then make it possible to obtain the following equation, given that the Mellin-Laplace transform of a convolution integer of two terms is equal to the product of the Mellin-Laplace transforms of each of those two terms:

$\begin{matrix} {Y_{sd}^{({p,n})} = {- {\sum\limits_{{i + j + k} = n}\; {A_{sd}^{({p,k})} \cdot \left( {{\int{{{G_{s}^{1{({p,i})}}\left( \overset{\rightarrow}{r} \right)} \cdot {{\delta\mu}_{a}\left( \overset{\rightarrow}{r} \right)} \cdot {G_{d}^{1{({p,j})}}\left( \overset{\rightarrow}{r} \right)}}{\overset{\rightarrow}{r}}}} + {\int{{{\overset{\rightarrow}{\nabla}{G_{s}^{1{({p,i})}}\left( \overset{\rightarrow}{r} \right)}} \cdot \delta}\; {{D\left( \overset{\rightarrow}{r} \right)} \cdot {\overset{\rightarrow}{\nabla}{G_{d}^{1{({p,j})}}\left( \overset{\rightarrow}{r} \right)}} \cdot {\overset{\rightarrow}{r}}}}}} \right)}}}} & (11) \end{matrix}$

Equation (11) thus makes it possible to compute the Mellin-Laplace transform of order n of the corrected distribution Y_(sd) ^((p,n)) of the Mellin-Laplace transforms of the second time distribution A_(sd)(t) and Green functions G_(s) ¹(r), G_(d) ¹(r) approximating the first modeling function G_(sd)(t).

The medium 12 is discretized into a plurality M of voxels referenced m, with m being between 1 and M, so as to compute the Mellin-Laplace transforms of the Green functions G_(s) ¹(r), G_(d) ¹(r). The discretized Green functions G_(s) ¹(r), G_(d) ¹(r) for each voxel m are denoted G_(s) ¹(r_(m)), G_(d) ¹(r_(m)).

According to one alternative in which the instrument response IRF_(sd) is measured for each source-detector pair (s, d), during a calibration operation, the Mellin-Laplace transforms of the first distribution B_(sd) ^((p,n)) and the instrument response IRF_(sd) ^((p,n)) are computed for different values of the order n between 0 and n_(max), preferably for all of the successive values between 0 and n_(max).

The reconstruction of optical properties of the medium 12 is then done during step 140, so as for example to determine the factors {right arrow over (μ)}_(a) and {right arrow over (D)}, the terms μ_(a)(m) and D(m) of which, discretized for each voxel m, i.e. μ_(a)({right arrow over (r)}={right arrow over (r)}_(m)) et D({right arrow over (r)}={right arrow over (r)}_(m)), constitute the maps of the desired optical properties.

The reconstruction step then aims to resolve the following matrix system:

Y=WX,  (12)

where Y represents an observation vector, W represents a transition matrix, and X represents a vector of the unknowns.

The observation vector Y contains the Nn Mellin-Laplace transforms for the corrected distribution Y_(sd) ^((p,n)), with Nn equal to n_(max)+1, computed for all of the successive values of the order n between 0 and n_(max) and for the considered source-detector (s, d) pairs.

For each triplet (s, d, n) of the indices s, d and the order n, an index I is defined as follows:

I=(s−1)×Nd×Nn+(d−1)×Nn+n  (13)

where Nd is the number of considered detectors 16.

The maximum value of the index I is equal to Imax, such that:

I _(max) =Ns×Nd×Nn  (14)

The observation vector Y then comprises Imax lines.

The passage matrix W includes a first portion W^(μ) comprising first terms denoted W^(μ)(I,m) and a second portion W^(D) comprising second terms denoted W^(D)(I,m). This matrix is called the sensitivity matrix of the measurements Y to the optical properties X.

The first terms W^(μ)(I,m) verify the following equation:

$\begin{matrix} {{W^{\mu}\left( {I,m} \right)} = {- {\sum\limits_{{i + j + k} = n}\; {A_{sd}^{({p,k})} \cdot {G_{s}^{1{({p,i})}}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {G_{d}^{1{({p,j})}}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot V_{m}}}}} & (15) \end{matrix}$

and the second terms W^(D)(I,m) verify the following equation:

$\begin{matrix} {{W^{D}\left( {I,m} \right)} = {- {\sum\limits_{{i + j + k} = n}\; {A_{sd}^{({p,k})} \cdot {\overset{\rightarrow}{\nabla}{G_{s}^{1{({p,i})}}\left( {\overset{\rightarrow}{r}}_{m} \right)}} \cdot {\overset{\rightarrow}{\nabla}{G_{d}^{1{({p,j})}}\left( {\overset{\rightarrow}{r}}_{m} \right)}} \cdot V_{m}}}}} & (16) \end{matrix}$

where V_(m) represents a volume element surrounding the mesh m, such as the volume of the Voronoi cell. In other words, V_(m) represents the volume of the voxel m.

According to this embodiment, during each iteration, the Mellin-Laplace transforms, of various orders, of G_(s) ¹(t) and G_(d) ¹(t) are determined, in particular using the following expression:

$\begin{matrix} {{{{- {\nabla{D\left( \overset{\rightarrow}{r} \right)}}}{\nabla{G_{s}^{1{({p,n})}}\left( \overset{\rightarrow}{r} \right)}}} + {\left( {{\mu_{a}\left( \overset{\rightarrow}{r} \right)} + \frac{p}{c}} \right){G_{s}^{1{({p,n})}}\left( \overset{\rightarrow}{r} \right)}}} = \left\{ \begin{matrix} {\delta (r)} & {{{if}\mspace{14mu} n} = 0} \\ {\frac{p}{c}{G_{s}^{1{({p,{n - 1}})}}\left( \overset{\rightarrow}{r} \right)}} & {{{if}\mspace{14mu} n} > 0} \end{matrix} \right.} & (17) \end{matrix}$

Thus, during each iteration, G_(s) ^(1(p,n=0)) is determined, then the transforms of higher order are determined iteratively, using the above expression when n>0. During the first iteration, D=D⁰ and μ_(a)=μ_(a) ⁰ are used, and during subsequent iterations, D=D¹ and μ_(a)=μ_(a) ¹ are used. The same reasoning naturally applies to determine the Mellin-Laplace transforms of the function G_(d) ¹(t).

The transition matrix W then comprises Imax lines and 2M columns.

Each term W^(μ)(I,m), W^(D)(I,m) of the transition matrix W is determined by modeling, upon each iteration, as a function of the optical properties determined during the preceding iteration, or during the first iteration, as a function of optical properties initialized by the operator. This initialization is done for example considering a homogenous medium 12.

The vector of the unknowns X comprises 2M lines and one column, and contains the unknowns μ_(a)(m) and D(m) for each of the M voxels. The first M lines correspond to the unknowns μ_(a)(m) and the following M lines of the vector X correspond to the unknowns D(m).

The inversion of the transition matrix W is done using inversion algorithms well known by those skilled in the art, such as a gradient descent algorithm, an algebraic reconstruction technique (ART), a singular value decomposition (SVD), or a conjugated gradients method. The process stops when a convergence criterion has been met, for example when the distance between two successive vectors X is below a predetermined threshold.

FIG. 4 is a set of images 200 to 208 showing a sensitivity J_(sd) ^((p,n)) defined by the equation:

$\begin{matrix} {{J_{sd}^{({p,n})}\left( \overset{\rightarrow}{r} \right)} = \frac{\partial{Y_{sd}^{({p,n})}\left( \overset{\rightarrow}{r} \right)}}{\partial{\mu \left( \overset{\rightarrow}{r} \right)}}} & (18) \end{matrix}$

for the Mellin-Laplace transforms for all of the corrected time distributions Y_(sd) ^((p,n)), with twenty-one sources 14 and twenty-one detectors 16, respectively symbolized by a down arrow and an up arrow. The space between successive sources 14 and detectors 16 is approximately one millimeter.

Each image 200 to 208 corresponding to a respective increasing value of the order n is between 0 and 8, the image 200 being associated with the order 0 and the image 208 being associated with the order 8. In each image 200 to 208, the set of corrected time distributions Y_(sd) ^((p,n)) corresponds to a banana-shaped light area 210.

One skilled in the art can then note that the higher the value of the order n, the better the sensitivity is in a deep area of the medium 12, for example a depth of more than 1 cm. In other words, the higher the value of the order n, the more the reconstruction system 10 using the method according to the invention can observe the deep area of the medium 12.

The reconstruction system 10 and the reconstruction method according to examples of the invention allow more precise localization of absorbers positioned deep down than the imaging system and reconstruction method of the state of the art, as shown by FIGS. 5 and 6. FIG. 5 is a view of an image of a medium comprising two absorbers positioned at a depth of 1 cm and 2 cm, respectively, referenced by circles 220 and 230, respectively, reconstructed with an imaging system and a reconstruction method of the state of the art, whereas FIG. 6 is a view of an image of the same medium reconstructed with an imaging system and reconstruction method according to examples of the invention. By comparing FIGS. 5 and 6, one skilled in the art can note that the absorbers are localized more precisely in the case of the imaging system and reconstruction method according to examples of the invention, in particular for the absorber positioned 2 cm deep.

The reconstruction of the optical properties using the combination of a plurality of Mellin-Laplace transforms of the magnitude comprising the processed distribution, in particular using the combination of the n_(max)+1 Mellin-Laplace transforms Y_(sd) ^((p,n)), makes it possible to converge toward the solution more quickly, so as to reduce the processing time needed to reconstruct the optical properties of the medium.

Another advantage of such a combination is that it makes it possible to account for the diffused photons detected shortly after the light pulse, i.e. just 1 or 2 ns after the light pulse, also called prompt photons, using a Mellin-Laplace transform with small orders, such as values of the order n between 0 and 5, while also accounting for the so-called delayed photons, i.e. detected several nanoseconds, or even more than 5 ns, after the light pulse. By using Mellin-Laplace transforms of different orders to perform the reconstruction, both the so-called prompt and delayed photons are taken into account, the latter generally coming from greater depths. It is therefore advantageous to perform the reconstruction using input data combining the Mellin-Laplace transforms of different orders. “Input data” refers to the observed data, i.e. the terms making up the observation vector Y.

We now describe another example according to which the corresponding instrument response IRF_(sd)(t) is determined for each source-detector pair.

The reconstruction is done using an iterative method, by performing a measurement B_(sd)(t) for one or more source-detector pair(s).

For each source-detector pair, the following equation is then obtained:

B _(sd)(t)−B _(sd) ¹(t)=−S _(s)(t)* (∫G _(s) ¹({right arrow over (r)},t)*δμ_(a)({right arrow over (r)},t)* G _(d) ¹({right arrow over (r)},t)d{right arrow over (r)}+∫{right arrow over (∇)}G _(s) ¹({right arrow over (r)},t)*δD({right arrow over (r)},t)*{right arrow over (∇)}G _(d) ¹({right arrow over (r)},t)d{right arrow over (r)})*D _(d)(t)  (19)

where:

-   -   B_(sd) ¹ represents a modeled measurement considering that the         medium has the absorption μ_(a) ¹(r) and diffusion D_(a) ¹(r)         coefficients determined during the previous iteration, the         values μ_(a) ¹(r) and D_(a) ¹(r) being initialized during the         first iteration by a first estimate done by the operator,     -   the coefficients δμ_(a)({right arrow over (r)},t) and δD({right         arrow over (r)},t) represent the difference between the optical         properties of the medium (μ_(a), D) and the initial optical         properties (μ_(a) ¹, D¹), or those resulting from the previous         iteration.

Similarly to the previous example, the aim is to minimize δμ_(a)({right arrow over (r)},t) and δD({right arrow over (r)},t).

If B_(sd)(t) is a measurement done on the medium to be characterized MC for a given source-detector pair sd, the Mellin-Laplace transforms are determined of a function Y_(sd), said transforms assuming the form:

$\begin{matrix} {Y_{s,d}^{({p,n})} = \frac{B_{s,d}^{({p,n})} - {\sum\limits_{i = 0}^{n - 1}\; {{IRF}_{s,d}^{({p,{n - i}})}B_{s,d}^{({p,i})}}}}{I_{s,d}^{({p,0})}}} & (20) \end{matrix}$

Thus, as a function of the Mellin-Laplace transforms of the instrument response IRF_(s,d) ^((p,k)), with 0≦k≦n, and the measurement B_(s,d) ^((p,k)) with 0≦k≦n, one determines Y_(s,d) ^((p,n)):

$\begin{matrix} {Y_{s,d}^{({p,n})} = \left( {{\int_{\Omega}{\sum\limits_{{j + k} = n}\; {{{G_{s}^{1{({p,j})}}\left( \overset{\rightarrow}{r} \right)} \cdot {{\delta\mu}_{a}\left( \overset{\rightarrow}{r} \right)} \cdot {G_{d}^{1^{({p,k})}}\left( \overset{\rightarrow}{r} \right)}}\ {\overset{\rightarrow}{r}}}}} + {\int_{\Omega}^{\;}{\sum\limits_{{j + k} = n}\; {{{G_{s}^{1{({p,j})}}\left( \overset{\rightarrow}{r} \right)} \cdot \delta}\; {{D\left( \overset{\rightarrow}{r} \right)} \cdot {G_{d}^{1^{({p,k})}}\left( \overset{\rightarrow}{r} \right)}}\ {\overset{\rightarrow}{r}}}}}} \right)} & (21) \end{matrix}$

After discretization of the medium in M voxels m, this expression becomes:

$\begin{matrix} {Y_{s,d}^{({p,n})} = {\sum\limits_{m = 1}^{M}\; \left( {{\sum\limits_{{j + k} = n}\; {{{G_{s}^{1{({p,j})}}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {{\delta\mu}_{a}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {G_{d}^{1{({p,k})}}\left( {\overset{\rightarrow}{r}}_{m} \right)}}V_{m}}} + {\sum\limits_{{j + k} = n}\; {{{G_{s}^{1{({p,j})}}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot \delta}\; {{D\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {G_{d}^{1{({p,k})}}\left( {\overset{\rightarrow}{r}}_{m} \right)}}V_{m}}}} \right)}} & (22) \end{matrix}$

An iterative inversion algorithm is then implemented to define the optical properties minimizing the vectors δμ_(a)({right arrow over (r)}_(m)) and δD({right arrow over (r)}_(m)), from a plurality of Mellin-Laplace transforms Y_(s,d) ^((p,n)), with different orders, obtained using measurements done with a plurality of source-detector pairs.

According to a further example, the Mellin-Laplace transform is used to establish a fluorescence map F({right arrow over (r)}), with F({right arrow over (r)})=μ_(a)({right arrow over (r)})η, η being the fluorescence output of the fluorophore localized in the volume d{right arrow over (r)}. A response function of the fluorophore F({right arrow over (r)},t) is defined, such that

$\begin{matrix} {{F\left( {\overset{\rightarrow}{r},t} \right)} = {{F\left( \overset{\rightarrow}{r} \right)}{\exp \left( {- \frac{t}{\tau}} \right)}}} & (23) \end{matrix}$

with τ designating the lifetime of the fluorophore.

Using an excitation source s and a detector d like those defined in the first embodiment, a time measurement B_(sd)(t) is done on the medium to be characterized MC.

This measurement is written:

B _(sd)(t)=IRF _(sd)(t)* ∫_(Ω) G _(s)({right arrow over (r)},t)*F({right arrow over (r)}, t)* G _(d)({right arrow over (r)},t).d{right arrow over (r)}  (24)

where G_(s)({right arrow over (r)},t) and G_(d)({right arrow over (r)},t) are Green functions representing the photon density between the source s and the point {right arrow over (r)} of the medium, and between the point {right arrow over (r)} of the medium and the detector d, respectively.

Since

$\begin{matrix} {\left\lbrack \frac{\exp \left( {{- t}/\tau} \right)}{\tau} \right\rbrack^{({p,n})} = {{\frac{p^{n}}{n!}{\int_{0}^{\infty}{t^{n}{\frac{\exp \left( {{- t}/\tau} \right)}{\tau} \cdot {t}}}}} = \left( {p\; \tau} \right)^{n}}} & (25) \end{matrix}$

we obtain:

F ^((p,n))({right arrow over (r)})=F({right arrow over (r)})(pτ)^(n)  (26)

The Mellin-Laplace transform of the measured time distribution B_(sd) is therefore:

$\begin{matrix} {B_{sd}^{({p,n})} = {\sum\limits_{{i + j + k + l} = n}\; {{IRF}_{sd}^{({p,i})} \cdot \left( {p\; \tau} \right)^{j} \cdot {\int_{\Omega}^{\;}{{G_{s}^{({p,k})}\left( \overset{\rightarrow}{r} \right)} \cdot {F\left( \overset{\rightarrow}{r} \right)} \cdot {G_{sd}^{({p,l})}\left( \overset{\rightarrow}{r} \right)} \cdot \ {\overset{\rightarrow}{r}}}}}}} & (27) \end{matrix}$

After discretization of the medium in M voxels m, one obtains:

$\begin{matrix} {B_{sd}^{({p,n})} = {\sum\limits_{{i + j + k + l} = n}\; {{{IRF}_{sd}^{({p,i})} \cdot \left( {p\; \tau} \right)^{j}}{\sum\limits_{m = 1}^{M}\; {\left( {{{G_{s}^{({p,k})}\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {F\left( {\overset{\rightarrow}{r}}_{m} \right)} \cdot {G_{sd}^{({p,l})}\left( {\overset{\rightarrow}{r}}_{m} \right)}}V_{m}} \right).}}}}} & (28) \end{matrix}$

Lastly, the system is inverted after discretization of the medium and voxels. An inversion algorithm is then implemented, such as one of the known algorithms previously mentioned, to define the vector F({right arrow over (r)}_(m)) corresponding to the desired fluorescence map, from the plurality of Mellin-Laplace transforms Y_(s,d) ^((p,n)), with different orders, obtained using measurements done with a plurality of source-detector pairs sd.

Similarly to the above example, the combination of different Mellin-Laplace transforms with a magnitude Y_(s,d) ^((p,n)) corresponding to a source-detector pair, and in particular the use of different values of the order n, makes it possible to better account for the contributions of the prompt photons and delayed photons, the latter essentially coming from greater depths. 

1. A method for reconstructing the optical properties of a medium, using a reconstruction system comprising at least one radiation source capable of lighting the medium and at least one detector capable of receiving a signal transmitted by the medium, the method including the following steps: lighting the medium using the radiation source, receiving, by the detector, the signal transmitted by the medium, and processing, at least one source-detector pair, of a first distribution of the signal received by the detector, computing the Mellin-Laplace transform, for a given order and a given variable, of a magnitude comprising the first distribution, the order being an integer, the variable being a real number, and reconstructing optical properties of the medium using the Mellin-Laplace transform of said magnitude, wherein the computing step comprises computing a plurality of Mellin-Laplace transforms of said magnitude for distinct values of the order, and in that the reconstructing step is carried out from a combination of the plurality of Mellin-Laplace transforms.
 2. The method according to claim 1, wherein the computing step comprises the computation of at least one Mellin-Laplace transform of said magnitude for an order value greater than or equal to 5, preferably greater than or equal to
 8. 3. The method according to claim 2, wherein the computing step comprises the computation of n_(max)+1 Mellin-Laplace transforms, the order successively taking all values comprised between 0 and n_(max), n_(max) being greater than or equal to 5, preferably greater than or equal to
 8. 4. The method according to claim 1, wherein the value of the variable is between 1 ns⁻¹ and 20 ns⁻¹.
 5. The method according to claim 1, wherein the method further comprises the step of: determining, for at least one source-detector pair, a first modeling function of a diffusion signal of the light between the source and the detector in the medium, processing, for said at least one source-detector pair, a second distribution of a signal received by the detector for a reference medium, the received signal being transmitted by the reference medium following the lighting of said medium by the source, and determining, for at least one source-detector pair, a second modeling function of a diffusion signal of the light between the source and the detector in the reference medium, and wherein said magnitude depends on the first distribution, the first modeling function, the second distribution and the second modeling function.
 6. The method according to claim 5, wherein said magnitude is obtained by subtracting the product of the second distribution and the first modeling function from the product of the first distribution and the second modeling function.
 7. The method according to claim 5, wherein said magnitude is the ratio between the product of the first distribution and the second modeling function and the product of the second distribution and the first modeling function.
 8. The method according to claim 1, wherein the radiation source comprises a pulsed light source.
 9. The method according to claim 1, wherein the detector is a time-resolved detector.
 10. The method according to claim 1, wherein the optical properties include at least one element from amongst the group consisting of: light absorption properties, in particular, the absorption coefficient, diffusion properties, in particular, the reduced diffusion coefficient or the diffusion coefficient, and fluorescence properties, in particular, a response function of a fluorophore, or by a concentration of the fluorophore, or by a magnitude depending on the quantity of the fluorophore.
 11. A system for reconstructing optical properties of a medium, including: a radiation source capable of lighting the medium, a detector capable of receiving a signal transmitted by the medium, a processor processing a distribution of the signal received by the detector for at least one source-detector pair, a device computing the Mellin-Laplace transform, for a given order and a given variable, of a magnitude comprising the distribution, the order being an integer, the variable being a real number, and for a reconstructing device reconstructing the optical properties of the medium using the Mellin-Laplace transform of said magnitude, wherein the computing device is capable of computing the plurality of Mellin-Laplace transforms of said magnitude for distinct values of the order, and in that the reconstruction means are capable of reconstructing the optical properties of the medium from a combination of the plurality of Mellin-Laplace transforms.
 12. The system according to claim 11, wherein the computing device is capable of computing at least one Mellin-Laplace transform of said magnitude for an order value greater than or equal to 5, preferably greater than or equal to
 8. 